Several large-scale whole genome sequencing (WGS) and omics efforts are underway. Although these programs are making strides towards including ancestrally diverse populations, there is still a notable gap in analyses of these data that leverage diversity to empower discovery and improve our understanding of genotypic and phenotypic architecture across all populations. Substantial European bias persists in ongoing large scale WGS and omics efforts. Differences in genetic variation among ancestrally diverse groups are well-known, and although data are very limited, gene expression, methylation, and metabolites also differ among ancestrally diverse populations. The PAGE Study has been continuously funded by the NIH since 2008 to study genomic variation to advance our understanding of population architecture of complex genetic traits and diseases, particularly in the presence of ancestral diversity. We have established PAGE at the forefront of discovery and fine-mapping across 40 primary and >200 secondary complex traits and diseases in ancestrally diverse populations, and have led the way in array (e.g. the Multi-Ethnic Global Array, Global Screening Array) and statistical methods development specific to these diverse populations. Additionally, PAGE has served as a valuable resource to the scientific community, placing a high priority on quickly disseminating allele frequency data, GWAS summary statistics, study findings, and analytical software. PAGE Phase III (PAGE III) will include existing genetics and genomics data from more than 120,000 diverse individuals from six well characterized cohorts/biobanks. In PAGE III, we propose to extend and continue our invaluable work to date to 1) identify and characterize genetic variants that influence complex traits and diseases in ancestrally diverse individuals using both WGS data (n=>50,000) and imputed genotyping data (n=124,000), 2) integrate information on sequence variation and omics to better understand the genetic underpinnings of complex traits in the diverse PAGE participants, and 3) characterize biological pathways underlying disease risk both within and between populations. For Aim 1, we will use recently generated sequence data to impute the largest sample of diverse participants every considered for the genetics of complex traits. For Aim 2, we will use newly existing data to impute gene expression, DNA methylation and metabolomics data into the remainder of PAGE samples, which will involve both extension and development of current methodology to ensure suitability for ancestrally diverse populations. Aim 3 will focus on integration of all omics data to inform discovery of novel pathways and the genetic basis of complex diseases and elucidate the molecular drivers of disease etiologies across diverse PAGE populations. As a continuation of our ground-breaking highly-successful work in PAGE over the last decade, our proposal offers major advances towards understanding the genetic and genomic components underlying biological mechanisms of disease, and translational medicine in adversely affected and understudied diverse populations.